Classification and identification of electric shock current for safety operation in power distribution network

Abstract

Electric shock current identification is essential for the safety in power distribution network. Moreover, as different categories of object have different electric shock current characteristic, a classification model for shock current is essential to be proposed before identification. Therefore, the authors proposed a two-stage framework, including the AdaBoost for the classification and an improved support vector machine (SVM) method for the identification. In the classification stage, the AdaBoost learns the hidden pattern of different electric shock current and generates a predictive model for current classification. Based on the classification results, a fusion method called SVM–NN is proposed in the identification stage, which is based on SVM and neural network (NN) to make fusion determination. The SVM–NN takes advantages of SVM and NN for integration analysis. Based on real data, these classification and identification methods are evaluated. Results show that the proposed method can significantly improve the identification accuracy of electric shock current signal comparing to traditional methods.

Keywords

• pattern classification
• electric shocks
• support vector machines
• learning (artificial intelligence)
• safety operation
• power distribution network
• electric shock current identification
• different electric shock current characteristic
• classification model
• two-stage framework
• adaboost
• improved support vector machine method
• classification stage
• predictive model
• current classification
• fusion method
• svm–nn
• identification stage
• neural network
• identification accuracy
• electric shock current signal comparing